Inactivation of Escherichia coli, Salmonella enterica, and Listeria monocytogenes using the Contamination Sanitization Inspection and Disinfection (CSI-D) device.

The Contamination Sanitization Inspection and Disinfection (CSI-D) device is a handheld fluorescence-based imaging system designed to disinfect food contact surfaces using ultraviolet-C (UVC) illumination. This study aimed to determine the optimal CSI-D parameters (i.e., UVC exposure time and intensity) for the inactivation of the following foodborne bacteria plated on non-selective media: generic Escherichia coli (indicator organism) and the pathogens enterohemorrhagic E. coli, enterotoxigenic E. coli, Salmonella enterica, and Listeria monocytogenes. Each bacterial strain was spread-plated on non-selective agar and exposed to high-intensity (10 mW/cm2) or low-intensity (5 mW/cm2) UVC for 1-5 s. Control plates were not exposed to UVC. The plates were incubated overnight at 37 °C and then enumerated. Three trials for each bacterial strain were conducted. Statistical analysis was carried out to determine if there were significant differences in bacterial growth between UVC intensities and exposure times. Overall, exposure to low or high intensity for 3-5 s resulted in consistent inhibition of bacterial growth, with reductions of 99.9-100 % for E. coli, 96.8-100 % for S. enterica, and 99.2-100 % for L. monocytogenes. The 1 s exposure time showed inconsistent results, with a 66.0-100 % reduction in growth depending on the intensity and bacterial strain. When the results for all strains within each species were combined, the 3-5 s exposure times showed significantly greater (p < 0.05) growth inhibition than the 1 s exposure time. However, there were no significant differences (p > 0.05) in growth inhibition between the high and low UVC intensities. The results of this study show that, in pure culture conditions, exposure to UVC with the CSI-D device for ≥3 s is required to achieve consistent reduction of E. coli, S. enterica, and L. monocytogenes.

A Novel Machine-Learning Framework Based on a Hierarchy of Dispute Models for the Identification of Fish Species Using Multi-Mode Spectroscopy.

Seafood mislabeling rates of approximately 20% have been reported globally. Traditional methods for fish species identification, such as DNA analysis and polymerase chain reaction (PCR), are expensive and time-consuming, and require skilled technicians and specialized equipment. The combination of spectroscopy and machine learning presents a promising approach to overcome these challenges. In our study, we took a comprehensive approach by considering a total of 43 different fish species and employing three modes of spectroscopy: fluorescence (Fluor), and reflectance in the visible near-infrared (VNIR) and short-wave near-infrared (SWIR). To achieve higher accuracies, we developed a novel machine-learning framework, where groups of similar fish types were identified and specialized classifiers were trained for each group. The incorporation of global (single artificial intelligence for all species) and dispute classification models created a hierarchical decision process, yielding higher performances. For Fluor, VNIR, and SWIR, accuracies increased from 80%, 75%, and 49% to 83%, 81%, and 58%, respectively. Furthermore, certain species witnessed remarkable performance enhancements of up to 40% in single-mode identification. The fusion of all three spectroscopic modes further boosted the performance of the best single mode, averaged over all species, by 9%. Fish species mislabeling not only poses health-related risks due to contaminants, toxins, and allergens that could be life-threatening, but also gives rise to economic and environmental hazards and loss of nutritional benefits. Our proposed method can detect fish fraud as a real-time alternative to DNA barcoding and other standard methods. The hierarchical system of dispute models proposed in this work is a novel machine-learning tool not limited to this application, and can improve accuracy in any classification problem which contains a large number of classes.

Toward an Intelligent Blockchain IoT-Enabled Fish Supply Chain: A Review and Conceptual Framework.

The fish industry experiences substantial illegal, unreported, and unregulated (IUU) activities within traditional supply chain systems. Blockchain technology and the Internet of Things (IoT) are expected to transform the fish supply chain (SC) by incorporating distributed ledger technology (DLT) to build trustworthy, transparent, decentralized traceability systems that promote secure data sharing and employ IUU prevention and detection methods. We have reviewed current research efforts directed toward incorporating Blockchain in fish SC systems. We have discussed traceability in both traditional and smart SC systems that make use of Blockchain and IoT technologies. We demonstrated the key design considerations in terms of traceability in addition to a quality model to consider when designing smart Blockchain-based SC systems. In addition, we proposed an Intelligent Blockchain IoT-enabled fish SC framework that uses DLT for the trackability and traceability of fish products throughout harvesting, processing, packaging, shipping, and distribution to final delivery. More precisely, the proposed framework should be able to provide valuable and timely information that can be used to track and trace the fish product and verify its authenticity throughout the chain. Unlike other work, we have investigated the benefits of integrating machine learning (ML) into Blockchain IoT-enabled SC systems, focusing the discussion on the role of ML in fish quality, freshness assessment and fraud detection.

Rapid Assessment of Fish Freshness for Multiple Supply-Chain Nodes Using Multi-Mode Spectroscopy and Fusion-Based Artificial Intelligence.

This study is directed towards developing a fast, non-destructive, and easy-to-use handheld multimode spectroscopic system for fish quality assessment. We apply data fusion of visible near infra-red (VIS-NIR) and short wave infra-red (SWIR) reflectance and fluorescence (FL) spectroscopy data features to classify fish from fresh to spoiled condition. Farmed Atlantic and wild coho and chinook salmon and sablefish fillets were measured. Three hundred measurement points on each of four fillets were taken every two days over 14 days for a total of 8400 measurements for each spectral mode. Multiple machine learning techniques including principal component analysis, self-organized maps, linear and quadratic discriminant analyses, k-nearest neighbors, random forest, support vector machine, and linear regression, as well as ensemble and majority voting methods, were used to explore spectroscopy data measured on fillets and to train classification models to predict freshness. Our results show that multi-mode spectroscopy achieves 95% accuracy, improving the accuracies of the FL, VIS-NIR and SWIR single-mode spectroscopies by 26, 10 and 9%, respectively. We conclude that multi-mode spectroscopy and data fusion analysis has the potential to accurately assess freshness and predict shelf life for fish fillets and recommend this study be expanded to a larger number of species in the future.

Application of image processing and transfer learning for the detection of rust disease.

Plant diseases introduce significant yield and quality losses to the food production industry, worldwide. Early identification of an epidemic could lead to more effective management of the disease and potentially reduce yield loss and limit excessive input costs. Image processing and deep learning techniques have shown promising results in distinguishing healthy and infected plants at early stages. In this paper, the potential of four convolutional neural network models, including Xception, Residual Networks (ResNet)50, EfficientNetB4, and MobileNet, in the detection of rust disease on three commercially important field crops was evaluated. A dataset of 857 positive and 907 negative samples captured in the field and greenhouse environments were used. Training and testing of the algorithms were conducted using 70% and 30% of the data, respectively where the performance of different optimizers and learning rates were tested. Results indicated that EfficientNetB4 model was the most accurate model (average accuracy = 94.29%) in the disease detection followed by ResNet50 (average accuracy = 93.52%). Adaptive moment estimation (Adam) optimizer and learning rate of 0.001 outperformed all other corresponding hyperparameters. The findings from this study provide insights into the development of tools and gadgets useful in the automated detection of rust disease required for precision spraying.

Combining deep learning and fluorescence imaging to automatically identify fecal contamination on meat carcasses.

Food safety and foodborne diseases are significant global public health concerns. Meat and poultry carcasses can be contaminated by pathogens like E. coli and salmonella, by contact with animal fecal matter and ingesta during slaughter and processing. Since fecal matter and ingesta can host these pathogens, detection, and excision of contaminated regions on meat surfaces is crucial. Fluorescence imaging has proven its potential for the detection of fecal residue but requires expertise to interpret. In order to be used by meat cutters without special training, automated detection is needed. This study used fluorescence imaging and deep learning algorithms to automatically detect and segment areas of fecal matter in carcass images using EfficientNet-B0 to determine which meat surface images showed fecal contamination and then U-Net to precisely segment the areas of contamination. The EfficientNet-B0 model achieved a 97.32% accuracy (precision 97.66%, recall 97.06%, specificity 97.59%, F-score 97.35%) for discriminating clean and contaminated areas on carcasses. U-Net segmented areas with fecal residue with an intersection over union (IoU) score of 89.34% (precision 92.95%, recall 95.84%, specificity 99.79%, F-score 94.37%, and AUC 99.54%). These results demonstrate that the combination of deep learning and fluorescence imaging techniques can improve food safety assurance by allowing the industry to use CSI-D fluorescence imaging to train employees in trimming carcasses as part of their Hazard Analysis Critical Control Point zero-tolerance plan.

FootAssure: A multimodal, in-home wound detection device for diabetic peripheral neuropathy.

Currently, there is no single technology capable of assessing all the multitude of factors associated with peripheral complications of diabetic neuropathy. In this work, a multimodal wound detection system is proposed to help facilitate in-home examinations, utilizing a combination of thermal, multi-spectral 3D imaging modalities. The proposed system is capable of the 3D surface rendering of the foot and would overlay thermal, blood oxygenation, besides other skin health information to aid with foot health monitoring. Examples of biomarkers include pre-ulcer formation, blood circulation, temperature change, oxygenation, swelling, blisters/ulcer formation and healing, and toe health.

Handheld Multispectral Fluorescence Imaging System to Detect and Disinfect Surface Contamination.

Contamination inspection is an ongoing concern for food distributors, restaurant owners, caterers, and others who handle food. Food contamination must be prevented, and zero tolerance legal requirements and damage to the reputation of institutions or restaurants can be very costly. This paper introduces a new handheld fluorescence-based imaging system that can rapidly detect, disinfect, and document invisible organic residues and biofilms which may host pathogens. The contamination, sanitization inspection, and disinfection (CSI-D) system uses light at two fluorescence excitation wavelengths, ultraviolet C (UVC) at 275 nm and violet at 405 nm, for the detection of organic residues, including saliva and respiratory droplets. The 275 nm light is also utilized to disinfect pathogens commonly found within the contaminated residues. Efficacy testing of the neutralizing effects of the ultraviolet light was conducted for Aspergillus fumigatus, Streptococcus pneumoniae, and the influenza A virus (a fungus, a bacterium, and a virus, respectively, each commonly found in saliva and respiratory droplets). After the exposure to UVC light from the CSI-D, all three pathogens experienced deactivation (> 99.99%) in under ten seconds. Up to five-log reductions have also been shown within 10 s of UVC irradiation from the CSI-D system.

Classification and Assessment of Hand Arthritis Stage using Support Vector Machine.

Arthritis is one of the most common health problems affecting people around the world. The goal of the work presented work is to classify and categorizing hand arthritis stages for patients, who may be developing or have developed hand arthritis, using machine learning. Stage classification was done using finger border detection, developed curvature analysis, principal components analysis, support vector machine and K-nearest neighbor algorithms. The outcome of this work showed that the proposed method can classify subject finger proximal interphalangeal joints (PIP) and distal interphalangeal joints (DIP) into stage classes with promising accuracy, especially for binary classification.

Review of the potential of optical technologies for cancer diagnosis in neurosurgery: a step toward intraoperative neurophotonics.

Advances in image-guided therapy enable physicians to obtain real-time information on neurological disorders such as brain tumors to improve resection accuracy. Image guidance data include the location, size, shape, type, and extent of tumors. Recent technological advances in neurophotonic engineering have enabled the development of techniques for minimally invasive neurosurgery. Incorporation of these methods in intraoperative imaging decreases surgical procedure time and allows neurosurgeons to find remaining or hidden tumor or epileptic lesions. This facilitates more complete resection and improved topology information for postsurgical therapy (i.e., radiation). We review the clinical application of recent advances in neurophotonic technologies including Raman spectroscopy, thermal imaging, optical coherence tomography, and fluorescence spectroscopy, highlighting the importance of these technologies in live intraoperative tissue mapping during neurosurgery. While these technologies need further validation in larger clinical trials, they show remarkable promise in their ability to help surgeons to better visualize the areas of abnormality and enable safe and successful removal of malignancies.

Real time optical Biopsy: Time-resolved Fluorescence Spectroscopy instrumentation and validation.

The Time-resolved fluorescence spectroscopy (TR-FS) has the potential to differentiate tumor and normal tissue in real time during surgical excision. In this manuscript, we describe the design of a novel TR-FS device, along with preliminary data on detection accuracy for fluorophores in a mixture. The instrument is capable of near real-time fluorescence lifetime acquisition in multiple spectral bands and analysis. It is also able to recover fluorescence lifetime with sub-20ps accuracy as validated with individual organic fluorescence dyes and dye mixtures yielding lifetime values for standard fluorescence dyes that closely match with published data. We also show that TR-FS is able to quantify the relative concentration of fluorescence dyes in a mixture by the unmixing of lifetime decays. We show that the TR-FS prototype is able to identify in near-real time the concentrations of dyes in a complex mixture based on previously trained data. As a result, we demonstrate that in complex mixtures of fluorophores, the relative concentration information is encoded in the fluorescence lifetime across multiple spectral bands. We show for the first time the temporal and spectral measurements of a mixture of fluorochromes and the ability to differentiate relative concentrations of each fluorochrome mixture in real time.

Separating melanin from hemodynamics in nevi using multimode hyperspectral dermoscopy and spatial frequency domain spectroscopy.

Changes in the pattern and distribution of both melanocytes (pigment producing) and vasculature (hemoglobin containing) are important in distinguishing melanocytic proliferations. The ability to accurately measure melanin distribution at different depths and to distinguish it from hemoglobin is clearly important when assessing pigmented lesions (benign versus malignant). We have developed a multimode hyperspectral dermoscope (SkinSpect™) able to more accurately image both melanin and hemoglobin distribution in skin. SkinSpect uses both hyperspectral and polarization-sensitive measurements. SkinSpect's higher accuracy has been obtained by correcting for the effect of melanin absorption on hemoglobin absorption in measurements of melanocytic nevi. In vivo human skin pigmented nevi (N=20) were evaluated with the SkinSpect, and measured melanin and hemoglobin concentrations were compared with spatial frequency domain spectroscopy (SFDS) measurements. We confirm that both systems show low correlation of hemoglobin concentrations with regions containing different melanin concentrations (R=0.13 for SFDS, R=0.07 for SkinSpect).

Automatic detection and severity measurement of eczema using image processing.

Chronic skin diseases like eczema may lead to severe health and financial consequences for patients if not detected and controlled early. Early measurement of disease severity, combined with a recommendation for skin protection and use of appropriate medication can prevent the disease from worsening. Current diagnosis can be costly and time-consuming. In this paper, an automatic eczema detection and severity measurement model are presented using modern image processing and computer algorithm. The system can successfully detect regions of eczema and classify the identified region as mild or severe based on image color and texture feature. Then the model automatically measures skin parameters used in the most common assessment tool called "Eczema Area and Severity Index (EASI)," by computing eczema affected area score, eczema intensity score, and body region score of eczema allowing both patients and physicians to accurately assess the affected skin.

Toward development of mobile application for hand arthritis screening.

Arthritis is one of the most common health problems affecting people throughout the world. The goal of the work presented in this paper is to provide individuals, who may be developing or have developed arthritis, with a mobile application to assess and monitor the progress of their disease using their smartphone. The image processing algorithm includes finger border detection algorithm to monitor joint thickness and angular deviation abnormalities, which are common symptoms of arthritis. In this work, we have analyzed and compared gradient, thresholding and Canny algorithms for border detection. The effect of image spatial resolution (down-sampling) is also investigated. The results calculated based on 36 joint measurements show that the mean errors for gradient, thresholding, and Canny methods are 0.20, 2.13, and 2.03 mm, respectively. In addition, the average error for different image resolutions is analyzed and the minimum required resolution is determined for each method. The results confirm that recent smartphone imaging capabilities can provide enough accuracy for hand border detection and finger joint analysis based on gradient method.

Mesoscopic reflectance angular domain spectroscopic imaging.

The advancement of angular domain imaging in mesoscopic reflectance multispectral imaging is reported. The key component is an angular filter array that performs the angular filtration of the back-scattered photons and generates image contrast due to the variances in tissue optical properties. The proposed modality enables multispectral imaging of subsurface features for samples too thick for transillumination angular domain spectroscopic imaging (ADSI) approaches. The validation was carried out with tissue-mimicking phantoms with multiple absorptive features embedded below the surface. Multispectral images in the range of 666 to 888 nm clearly revealed the location of the features with the background scattering levels up to 20 cm⁻¹. The shape of the features was recoverable at depths of up to three to four times the transport mean free path. The spatial resolution was <1 mm and the field-of-view was larger than 2.5 cm x 30. cm. Furthermore, the attenuation spectra of measured absorptive features were successfully extracted. Target detectability and imaging quality with different background scattering levels, target depths, and illumination focal depths were discussed, as well as the capability of ADSI in reflectance optical mesoscopic imaging and its potential applications.

Polarization-sensitive hyperspectral imaging in vivo: a multimode dermoscope for skin analysis.

Attempts to understand the changes in the structure and physiology of human skin abnormalities by non-invasive optical imaging are aided by spectroscopic methods that quantify, at the molecular level, variations in tissue oxygenation and melanin distribution. However, current commercial and research systems to map hemoglobin and melanin do not correlate well with pathology for pigmented lesions or darker skin. We developed a multimode dermoscope that combines polarization and hyperspectral imaging with an efficient analytical model to map the distribution of specific skin bio-molecules. This corrects for the melanin-hemoglobin misestimation common to other systems, without resorting to complex and computationally intensive tissue optical models. For this system's proof of concept, human skin measurements on melanocytic nevus, vitiligo, and venous occlusion conditions were performed in volunteers. The resulting molecular distribution maps matched physiological and anatomical expectations, confirming a technologic approach that can be applied to next generation dermoscopes and having biological plausibility that is likely to appeal to dermatologists.

Nanohole-array-based device for 2D snapshot multispectral imaging.

We present a two-dimensional (2D) snapshot multispectral imager that utilizes the optical transmission characteristics of nanohole arrays (NHAs) in a gold film to resolve a mixture of input colors into multiple spectral bands. The multispectral device consists of blocks of NHAs, wherein each NHA has a unique periodicity that results in transmission resonances and minima in the visible and near-infrared regions. The multispectral device was illuminated over a wide spectral range, and the transmission was spectrally unmixed using a least-squares estimation algorithm. A NHA-based multispectral imaging system was built and tested in both reflection and transmission modes. The NHA-based multispectral imager was capable of extracting 2D multispectral images representative of four independent bands within the spectral range of 662 nm to 832 nm for a variety of targets. The multispectral device can potentially be integrated into a variety of imaging sensor systems.

Large area periodic, systematically changing, multishape nanostructures by laser interference lithography and cell response to these topographies.

The fabrication details to form large area systematically changing multishape nanoscale structures on a chip by laser interference lithography (LIL) are described. The feasibility of fabricating different geometries including dots, ellipses, holes, and elliptical holes in both x- and y- directions on a single substrate is shown by implementing a Lloyd's interferometer. The fabricated structures at different substrate positions with respect to exposure time, exposure angle and associated light intensity profile are analyzed. Experimental details related to the fabrication of symmetric and biaxial periodic nanostructures on photoresist, silicon surfaces, and ion milled glass substrates are presented. Primary rat calvarial osteoblasts were grown on ion-milled glass substrates with nanotopography with a periodicity of 1200 nm. Fluorescent microscopy revealed that cells formed adhesions sites coincident with the nanotopography after 24 h of growth on the substrates. The results suggest that laser LIL is an easy and inexpensive method to fabricate systematically changing nanostructures for cell adhesion studies. The effect of the different periodicities and transition structures can be studied on a single substrate to reduce the number of samples significantly.

Radial angular filter arrays for angle-resolved scattering spectroscopy.

The radial angular filter array (RAFA) consists of a series of radially-distributed micro-machined channels, where the long axes of the channels converge at a focal point. The high aspect ratio of each channel provides a means to reject photons with trajectories outside the acceptance angle of the channel. The output of the RAFA represents the angular distribution of photons emitted from the focal point. A series of RAFAs were designed, fabricated, and tested to evaluate the impact of device geometry, inter-channel cross talk, achromaticity, and channel leakage on device performance. As an application example, an RAFA was used together with an imaging spectrometer to capture angle-resolved spectra of turbid samples.

Transillumination hyperspectral imaging for histopathological examination of excised tissue.

Angular domain spectroscopic imaging (ADSI) is a novel technique for the detection and characterization of optical contrast in turbid media based on spectral characteristics. The imaging system employs a silicon micromachined angular filter array to reject scattered light traversing a specimen and an imaging spectrometer to capture and discriminate the largely remaining quasiballistic light based on spatial position and wavelength. The imaging modality results in hyperspectral shadowgrams containing two-dimensional (2D) spatial maps of spectral information. An ADSI system was constructed and its performance was evaluated in the near-infrared region on tissue-mimicking phantoms. Image-based spectral correlation analysis using transmission spectra and first order derivatives revealed that embedded optical targets could be resolved. The hyperspectral images obtained with ADSI were observed to depend on target concentration, target depth, and scattering level of the background medium. A similar analysis on a muscle and tumor sample dissected from a mouse resulted in spatially dependent optical transmission spectra that were distinct, which suggested that ADSI may find utility in classifying tissues in biomedical applications.